Name
Abstract | ||
|---|---|---|
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the usersu0027 movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters by using an efficient EM algorithm, which distributes over the users. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Social and Information Networks | Kernel (linear algebra),Data mining,Social network,Inference,Computer science,Expectation–maximization algorithm,Point process,Multinomial distribution,Artificial intelligence,Statistical model,Periodic graph (geometry),Machine learning |
DocType | Volume | Citations |
Journal | abs/1611.07710 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ali zarezade | 1 | 19 | 2.71 |
| Sina Jafarzadeh | 2 | 5 | 0.81 |
| Hamid R. Rabiee | 3 | 336 | 41.77 |